Geography 13

Lab 03: Distances and the Border Zone

Load library packages

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.4     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1

library(units)

## udunits database from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/units/share/udunits/udunits2.xml

library(USAboundaries)
library(rnaturalearth)
library(gghighlight)
library(ggrepel)
library(knitr)
library(readxl)
library(dplyr)

##Data

get_conus = function(data, var){
  filter(data, !get(var) %in% c("Hawaii", "Puerto Rico", "Alaska", "Guam"))
}
states_union = get_conus(us_states(), "name") %>% 
  st_union() %>% 
  st_cast("MULTILINESTRING")

states_combine = get_conus(us_states(), "name") %>% 
  st_combine() %>% 
  st_cast("MULTILINESTRING")

countries = st_as_sf(rnaturalearth::countries110)

Question 1

### Question 1.1 Define a Projection
eqdc= '+proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs'

### Question 1.2 Get USA State Boundaries
boundaries <- USAboundaries::us_states(resolution = "low") %>%
  get_conus("state_name") %>% 
  filter(name != "District of Columbia") %>% 
  select(name, geometry) %>% 
  st_transform(eqdc)

plot(boundaries)

### Question 1.3 Country Boundaries
countryBounds <- rnaturalearth::countries110 %>% 
  st_as_sf(countryBounds) %>% 
  filter(admin %in% c("Canada", "Mexico", "United States of America")) %>%            st_transform(eqdc) 

ggplot(countryBounds)+
  geom_sf()

### Question 1.4 Get City Locations
url = '~/github/geog-13-labs/data/uscities.csv'
uscities <- read_csv(url)

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   city = col_character(),
##   city_ascii = col_character(),
##   state_id = col_character(),
##   state_name = col_character(),
##   county_fips = col_character(),
##   county_name = col_character(),
##   lat = col_double(),
##   lng = col_double(),
##   population = col_double(),
##   density = col_double(),
##   source = col_character(),
##   military = col_logical(),
##   incorporated = col_logical(),
##   timezone = col_character(),
##   ranking = col_double(),
##   zips = col_character(),
##   id = col_double()
## )

bordercities <- uscities %>%
  get_conus("state_name") %>% 
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>% 
  st_transform(crs = st_crs(eqdc)) 

plot(bordercities)

## Warning: plotting the first 9 out of 15 attributes; use max.plot = 15 to plot
## all

Question 2

### Question 2.1 Distance to USA Border in km
resolvedBoundaries <- USAboundaries::us_states()%>% 
  get_conus("state_name") %>% 
  filter(name != "District of Columbia") %>% 
  st_as_sf() %>% 
  st_union() %>% 
  st_cast("MULTILINESTRING") %>% 
  st_transform(eqdc) 


bordercities2.1 <- bordercities
dist_to_USborder <- st_distance(bordercities2.1, resolvedBoundaries) %>% 
  set_units("km") 
  
distance <- bordercities2.1 %>% 
  mutate(dist_to_USborder = st_distance(bordercities2.1, resolvedBoundaries),
         dist_to_USborder = set_units(dist_to_USborder, "km"),
         dist_to_USborder = as.numeric(dist_to_USborder)) %>%
  select(city, state_name, dist_to_USborder) 

distance2.1 <- distance %>% 
  slice_max(dist_to_USborder, n = 5) %>% 
  st_drop_geometry()

knitr::kable(distance2.1, caption = "Five Cities Farthest From USA Border", col.names = c("City", "State", "Distance (km)")) 

Five Cities Farthest From USA Border

City	State	Distance (km)
Dresden	Kansas	1012.398
Herndon	Kansas	1007.763
Hill City	Kansas	1005.143
Atwood	Kansas	1004.754
Jennings	Kansas	1003.646

### Question 2.2 Distance to States in km
preservedBoundaries <- USAboundaries::us_states()%>% 
  get_conus("state_name") %>% 
  filter(name != "District of Columbia") %>% 
  st_combine() %>% 
  st_cast("MULTILINESTRING") %>% 
  st_transform(eqdc) 

bordercities2.2 <- bordercities
dist_to_stateborder <- st_distance(bordercities2.2,preservedBoundaries) %>% 
  set_units("km")

distance2.2 <- bordercities2.2 %>% 
   mutate(dist_to_stateborder = st_distance(bordercities2.2, preservedBoundaries),
         dist_to_stateborder = set_units(dist_to_stateborder, "km"),
         dist_to_stateborder = as.numeric(dist_to_stateborder)) %>%
  select(city, state_name, dist_to_stateborder) %>% 
  slice_max(dist_to_USborder, n = 5) %>% 
  st_drop_geometry()  
  
knitr::kable(distance2.2, caption = "Five Cities Farthest From Nearest State Boundary", col.names = c("City", "State", "Distance (km)")) 

Five Cities Farthest From Nearest State Boundary

City	State	Distance (km)
Dresden	Kansas	42.07713
Herndon	Kansas	10.34085
Hill City	Kansas	70.43168
Atwood	Kansas	21.32134
Jennings	Kansas	35.65318

### Question 2.3 Distance to Mexico in km
mexicoborder <- countryBounds %>% 
  filter(!admin %in% c("Canada", "United States of America")) %>% 
  st_union() %>% 
  st_cast("MULTILINESTRING")

bordercities2.3 <- bordercities

distance2.3 <- bordercities2.3 %>% 
  mutate(dist_to_mexborder = st_distance(bordercities2.3, mexicoborder),
         dist_to_mexborder = set_units(dist_to_mexborder, "km"),
         dist_to_mexborder = as.numeric(dist_to_mexborder)) %>%
  select(city, state_name, dist_to_mexborder) %>% 
  slice_max(dist_to_mexborder, n = 5) %>% 
  st_drop_geometry() 

knitr::kable(distance2.3, caption = "Five Cities Farthest From Mexican Border", col.names = c("City", "State", "Distance (km)")) 

Five Cities Farthest From Mexican Border

City	State	Distance (km)
Caribou	Maine	3250.334
Presque Isle	Maine	3234.570
Calais	Maine	3134.348
Passamaquoddy Pleasant Point	Maine	3127.869
Eastport	Maine	3125.624

### Question 2.4 Distance to Canada in km
canadaborder <- countryBounds %>% 
  filter(!admin %in% c("Mexico", "United States of America")) %>% 
  st_union() %>% 
  st_cast("MULTILINESTRING")

bordercities2.4 <- bordercities

distance2.4 <- bordercities2.4 %>% 
  mutate(dist_to_canadaborder = st_distance(bordercities2.4, canadaborder),
         dist_to_canadaborder = set_units(dist_to_canadaborder, "km"),
         dist_to_canadaborder = as.numeric(dist_to_canadaborder)) %>%
  select(city, state_name, dist_to_canadaborder) %>% 
  slice_max(dist_to_canadaborder, n = 5) %>% 
  st_drop_geometry()  
  
knitr::kable(distance2.4, caption = "Five Cities Farthest From Canadian Border", col.names = c("City", "State", "Distance (km)")) 

Five Cities Farthest From Canadian Border

City	State	Distance (km)
Guadalupe Guerra	Texas	2206.455
Fronton	Texas	2204.790
Fronton Ranchettes	Texas	2202.118
Evergreen	Texas	2202.020
Ramos	Texas	2201.882

Question 3

### Question 3.1 Data
outlineCONUS <- resolvedBoundaries

citiesbig10 <- uscities %>% 
  select(city, state_name, population, lat, lng) %>% 
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>% 
  st_transform(eqdc) %>% 
  slice_max(population, n = 10)

ggplot(countryBounds)+
  geom_sf()+
  geom_sf(data = boundaries, fill = "light blue")+
  geom_sf(data = outlineCONUS, aes(linetype = "solid", color = "navy"))+
   geom_sf(data = citiesbig10, fill = "navy", shape = 23)+
  geom_label_repel(data = citiesbig10, aes(geometry = geometry, label = city), stat = "sf_coordinates")+
  labs(title = "North America and Ten Largest USA Cities")

### Question 3.2 City Distance from USA Border
USAborder <- resolvedBoundaries

uscities3 <- uscities %>% 
  filter(!state_name %in% c("Alaska", "Hawaii", "Puerto Rico")) %>%  
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>% 
  st_transform(eqdc) 
  
uscities3 <- uscities3 %>% 
  mutate(distances = st_distance(uscities3, USAborder),
         distances = set_units(distances, "km"),
         distances = as.numeric(distances)) %>%
  select(city, state_name, population, distances)

fivefarcities_0 <- uscities3 %>% 
  slice_max(distances, n =5)

ggplot(countryBounds)+
  geom_sf()+
  geom_sf(data = boundaries)+
  geom_sf(data = uscities3, aes(col = distances), size = .08)+
  geom_sf(data = fivefarcities_0, fill = "coral", shape = 23)+
  geom_label_repel(data = fivefarcities_0, aes(geometry = geometry, label = city), stat = "sf_coordinates")+
  labs(title = "USA Citys in Relation to US Border")

### Question 3.3 City Distance from Respective State Boundary
stateboundaries <- preservedBoundaries

uscities3.2 <- uscities %>% 
  filter(!state_name %in% c("Alaska", "Hawaii", "Puerto Rico")) %>% 
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>% 
  st_transform(eqdc) 

uscities3.2 <- uscities3.2 %>% 
  mutate(distances = st_distance(uscities3.2, stateboundaries),
         distances = set_units(distances, "km"),
         distances = as.numeric(distances)) %>%
  select(city, state_name, population, distances)

fivefarcities <- uscities3.2 %>% 
  slice_max(distances, n =5)
  
ggplot(countryBounds)+
  geom_sf()+
  geom_sf(data = stateboundaries)+
  geom_sf(data = uscities3.2, aes(col = distances), size = .08)+
  geom_sf(data = fivefarcities, fill = "coral", shape = 23)+
  geom_label_repel(data = fivefarcities,aes(geometry = geometry, label = city), stat = "sf_coordinates")+
  labs(title = "USA Citys in Relation to State Borders")

### Question 3.4
canadaborder

## Geometry set for 1 feature 
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: -2393433 ymin: 262079.3 xmax: 2982938 ymax: 4936902
## CRS:           +proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs

## MULTILINESTRING ((2287673 1138790, 2342028 1143...

mexicoborder

## Geometry set for 1 feature 
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: -1871315 ymin: -2812859 xmax: 946875.8 ymax: -609928.8
## CRS:           +proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs

## MULTILINESTRING ((-111145.7 -1566400, -150611.3...

uscities3.4 <- uscities %>% 
  filter(!state_name %in% c("Alaska", "Hawaii", "Puerto Rico")) %>% 
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>% 
  st_transform(eqdc) 

uscities3.4 <- uscities3.4 %>% 
  mutate(distance_canada = st_distance(uscities3.4, canadaborder),
         distance_canada = set_units(distance_canada, "km"),
         distance_canada = as.numeric(distance_canada)) %>%
  mutate(distance_mex = st_distance(uscities3.4, mexicoborder),
         distance_mex = set_units(distance_mex, "km"),
         distance_mex = as.numeric(distance_mex)) %>% 
  mutate(abs_dist = abs(distance_canada - distance_mex)) %>% 
  select(city, state_name, population, distance_canada, distance_mex, abs_dist) 

cities3.4 <- uscities3.4 %>% 
  filter(abs_dist <= 100) %>% 
  slice_max(population, n =5)

ggplot(countryBounds)+
  geom_sf()+
  geom_sf(data = stateboundaries)+
  geom_sf(data = uscities3.4, aes(col=abs_dist))+
  gghighlight::gghighlight(abs_dist <= 100)+
  geom_sf(data = cities3.4, aes(col=population))+
  geom_label_repel(data = cities3.4,aes(geometry = geometry, label = city), stat = "sf_coordinates")+
  labs(title = "Equidistant Cities from Mexico and Canada")

## Warning: Could not calculate the predicate for layer 1, layer 2; ignored

Question 4

### Question 4.1 Border Zone Cities Data 
resolvedBoundaries

## Geometry set for 1 feature 
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: -2230934 ymin: -1612761 xmax: 2124154 ymax: 1271087
## CRS:           +proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs

## MULTILINESTRING ((2014802 727969.6, 2019874 725...

bordercities

## Simple feature collection with 27620 features and 15 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -2224960 ymin: -1601803 xmax: 2115970 ymax: 1275643
## CRS:           +proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs
## # A tibble: 27,620 x 16
##    city    city_ascii  state_id state_name   county_fips county_name  population
##  * <chr>   <chr>       <chr>    <chr>        <chr>       <chr>             <dbl>
##  1 New Yo… New York    NY       New York     36061       New York       18713220
##  2 Los An… Los Angeles CA       California   06037       Los Angeles    12750807
##  3 Chicago Chicago     IL       Illinois     17031       Cook            8604203
##  4 Miami   Miami       FL       Florida      12086       Miami-Dade      6445545
##  5 Dallas  Dallas      TX       Texas        48113       Dallas          5743938
##  6 Philad… Philadelph… PA       Pennsylvania 42101       Philadelphia    5649300
##  7 Houston Houston     TX       Texas        48201       Harris          5464251
##  8 Atlanta Atlanta     GA       Georgia      13121       Fulton          5449398
##  9 Washin… Washington  DC       District of… 11001       District of…    5379184
## 10 Boston  Boston      MA       Massachuset… 25025       Suffolk         4688346
## # … with 27,610 more rows, and 9 more variables: density <dbl>, source <chr>,
## #   military <lgl>, incorporated <lgl>, timezone <chr>, ranking <dbl>,
## #   zips <chr>, id <dbl>, geometry <POINT [m]>

zone100 <- bordercities %>% 
  mutate(distance = st_distance(bordercities, resolvedBoundaries),
         distance = set_units(distance, "km"),
         distance = as.numeric(distance)) %>%
  filter(distance <= 160) %>% 
  select(city, state_name, population, distance, geometry) %>% 
  st_drop_geometry() 


cities_in_zone <- nrow(zone100) 
population_sum <- sum(zone100$population)
percent_population <- (population_sum / sum(uscities$population) * 100) %>% 
  round(digits =2)

     
df1 <- data.frame(Description = c("Number of Cities Within Zone","Total Population Within Zone", "Percent of Total Population within Zone %"), Stats = c(cities_in_zone, population_sum, percent_population))

knitr::kable(df1, caption = "100 Mile Border Zone Statistics")

100 Mile Border Zone Statistics

Description	Stats
Number of Cities Within Zone	11981.00
Total Population Within Zone	259952558.00
Percent of Total Population within Zone %	64.33

### Question 4.2 Mapping the Border Zone
zonecities <- bordercities %>% 
  mutate(distance = st_distance(bordercities, resolvedBoundaries),
         distance = set_units(distance, "km"),
         distance = as.numeric(distance)) %>%
  select(city, state_name, population, distance, geometry) 

most_pop <- zonecities %>%
  filter(distance <= 160) %>% 
  group_by(state_name) %>% 
  slice_max(population)

ggplot(countryBounds)+
  geom_sf()+
  geom_sf(data = zonecities, aes(), col= "navy", size = .03)+
  geom_sf(data = most_pop, aes(), col = "coral", size = .5, shape = 23)+
  gghighlight::gghighlight(distance <= 160)+
  geom_sf(data = stateboundaries)+
  geom_label_repel(data = most_pop, aes(geometry = geometry, label = city), stat = "sf_coordinates")+
  labs(title = "Mapping the 100 Mile Border Zone With Most Populated City Per State")